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Machine Reading Comprehension

Machine Reading Comprehension is one of the key problems in Natural Language Understanding, where the task is to read and comprehend a given text passage, and then answer questions based on it.

Source: Making Neural Machine Reading Comprehension Faster

Papers

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Showing 451–475 of 555 papers

TitleStatusHype
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions—0
Cooperative Self-training of Machine Reading Comprehension—0
Cooperative Semi-Supervised Transfer Learning of Machine Reading Comprehension—0
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test—0
Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning—0
Cross-lingual and Cross-domain Evaluation of Machine Reading Comprehension with Squad and CALOR-Quest Corpora—0
Cross-lingual Machine Reading Comprehension with Language Branch Knowledge Distillation—0
Cross-Task Knowledge Transfer for Query-Based Text Summarization—0
CSReader at SemEval-2018 Task 11: Multiple Choice Question Answering as Textual Entailment—0
CSS: Combining Self-training and Self-supervised Learning for Few-shot Dialogue State Tracking—0
DADgraph: A Discourse-aware Dialogue Graph Neural Network for Multiparty Dialogue Machine Reading Comprehension—0
Data Augmentation for Biomedical Factoid Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Decoupled Transformer for Scalable Inference in Open-domain Question Answering—0
Deep Understanding based Multi-Document Machine Reading Comprehension—0
Detecting Causes of Stock Price Rise and Decline by Machine Reading Comprehension with BERT—0
Developing Dataset of Japanese Slot Filling Quizzes Designed for Evaluation of Machine Reading Comprehension—0
Dialog State Tracking: A Neural Reading Comprehension Approach—0
DIFM:An effective deep interaction and fusion model for sentence matching—0
Document-level Event Factuality Identification via Machine Reading Comprehension Frameworks with Transfer Learning—0
Does Structure Matter? Encoding Documents for Machine Reading Comprehension—0
Dual Co-Matching Network for Multi-choice Reading Comprehension—0
Dual Multi-head Co-attention for Multi-choice Reading Comprehension—0
DuReader\_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications—0
Dynamic Fusion Networks for Machine Reading Comprehension—0
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